Radical Numerics

12/ If models become function-aware, our defenses must too. To scale biodefense, we're scaling our team + partnerships. 🌉🌎🗼Join our mission in SF/Tokyo: radicalnumerics.ai/join-us 📝Sign up for early access: radicalnumerics.ai/waitlist?track…

11/ We’re now also pushing toward biodefense infrastructure: surveillance across time, space, and subsequence to map out human risks across metagenomic reads to whole viral genomes (early UI below). Screening can move from "have we seen it?" to "what could this do?"

1/ Leaders of OpenAI, Anthropic, & GDM signed a letter urging Congress to mandate DNA synthesis screening. But chatbots & agents can't read DNA. So we built Omnii to defend against natural & AI-designed pathogens. Blog: radicalnumerics.ai/blog/omnii-def… Letter: screendna.org

Our CEO Eric Nguyen @exnx sat down with @agihouse_org to talk about our origin, mission, and why we exist.

What if AI could read and write the language of life itself? In this interview with AGI House, @exnx, founder of @RadicalNumerics, breaks down how foundation models are being trained on DNA, RNA, and proteins — and why the next frontier of multimodal AI isn't just text or images, but the physical world of human biology.

Together with my co-founders Michael @MichaelPoli6, Stefano @Massastrello and Armin @athmsx, I am excited to announce @RadicalNumerics is emerging from stealth with a $50M seed round to build general biological intelligence. We’re also sharing an early preview of our new model Omnii, the most powerful genome language model to date. Omnii preview link: radicalnumerics.ai/blog/radical-n… At Radical Numerics, our mission is to master the code of life, and to drive the frontier of biological AI for both design and defense. This is our dual mandate, which comes from something our own team helped make possible. Our founding team trained Evo and Evo 2, the largest biological AI models (40B params) trained on DNA sequences. Trillions of tokens across all of life, from microbes to mammals. It’s fully open source, and created the field now known as generative genomics. Last year, scientists used Evo to generate the world’s first complete genome from scratch using AI. Turns out it was a bacteriophage—a type of virus. It functioned in the real world, and in this case it was harmless. But for us, it was a clear turning point. It showed that AI is no longer just analyzing biology. It is on the cusp of generating functional lifeforms. Eventually, AI will have the power to design and control life itself. That should make all of us incredibly excited, and incredibly uneasy. (Anyone can design DNA with a new function, and have it synthesized and delivered, like something from Amazon Prime). The same technology that will help us cure cancer is the very technology that might create the next global pandemic, or worse, allow the creation of bioweapons that can wipe out populations. We believe these forces are inseparable. If you work on the frontier of biology, you have to build technology to safeguard it from its misuse. Existing biosecurity tools are sorely losing the arms race, relying on outdated “have I seen this exact thing before?” style algorithms. We founded Radical Numerics to turn the tide. And we can’t do that by training on textbooks and natural language. We must understand the language of biology from the raw physical data itself, to reason across every molecule and modality, from DNA to proteins. The next frontier for AI goes far beyond chatbots or video generators to models that can understand and engineer life. Today, we’re previewing Omnii, which is already far surpassing Evo 2, and will continue improving as we scale and add new modalities (training now). 1. For human health, Omnii can read and write whole genomes (more on writing later). It’s state of the art (SOTA) on detecting causal variants for disease, and can rank Alzheimer's mutations zero-shot. We’re partnering with a diagnostics company to use Omnii for early cancer detection (pancreatic and multi-cancer). 2. For defense, Omnii is SOTA at detecting AI-generated pathogens. We benchmarked existing detection tools, and they simply can’t detect the AI-generated ones (“deepfake viruses”). We’re partnering with a US national lab to pilot Omnii for detecting the next pandemic, both natural and AI-generated. We have a data center full of Blackwells in construction now to build the most powerful biological AI models ever. This mission takes a new kind of AI lab that can actually scale on physical, biological data: new alignment research (mid/post training), scaling long context, building out mech interp teams to dissect what these models learn, new architectures and systems designs, all from the ground up. Our team is made up of AI researchers and scientists from top labs and institutions (e.g. Stanford, MIT, Google DeepMind), but more importantly, we all share the belief that this is the most important challenge of our lifetime. If you feel similarly, we are hiring. We aim to bring the brightest minds in AI and science together to save lives. Thanks to our partners on this journey, led by Emergence Capital @emergencecap, with Obvious Ventures @obviousvc, Triatomic @TriatomicCap , and Patrick Collison @patrickc. Our advisors include Eric Horvitz @erichorvitz, CSO of Microsoft, Chris Re @HazyResearch of Stanford, George Church @geochurch of Harvard, and Andrew Weber @AndyWeberNCB, former Assistant Secretary of Defense for Nuclear, Chemical and Biological Defense Programs. Fortune article: fortune.com/2026/06/15/exc… Jobs: radicalnumerics.ai/join-us

We're growing rapidly at @RadicalNumerics and scaling our core teams. Join us in building the next generation of scientific world models. We're hiring across a few roles, each with significant ownership and cross-functional scope: - Member of Technical Staff, Post-Training - Member of Technical Staff, Infrastructure and Training Systems - Member of Technical Staff, Pretraining Science - Member of Technical Staff, AI Bio - Member of Technical Staff, Biosecurity Our technology brings together numerics, systems engineering, and architecture design to tackle large-scale pretraining on scientific data. Our blogs (see below) give a flavor of the work. We believe that advancing capabilities must go hand-in-hand with advancing safety and biosecurity. The same systems that design biology must also help defend against it. Ping me or others in the team if you'd like to learn more. job-boards.greenhouse.io/radicalnumerics

Scaling scientific world models requires co-designing architectures, training objectives, and numerics. Today, we share the first posts in our series on low-precision pretraining, starting with NVIDIA's NVFP4 recipe for stable 4-bit training. Part 1: radicalnumerics.ai/blog/nvfp4-par… Part 2: radicalnumerics.ai/blog/nvfp4-par… We cover floating point fundamentals, heuristics, custom CUDA kernels, and stabilization techniques. Future entries will cover custom recipes and results on hybrid architectures.

Excited to share Phalanx, our new layer for sequence modeling! Each block communicates with its neighbor, like the shield cover of a neighboring hoplite. Phalanx can replace sliding window attention and trains faster than optimized baselines while maintaining quality.

More on Phalanx and our research kernel library: Blog: radicalnumerics.ai/blog/phalanx Code: github.com/RadicalNumeric… Report: radicalnumerics.ai/assets/phalanx…

Sliding window attention (SWA) is powering frontier hybrid models for efficiency. Is there something better? Introducing Phalanx, a faster and better quality drop-in replacement for sliding window attention (SWA). Phalanx is a new family of hardware and numerics-aware windowed layers designed with a focus on data locality and jagged, block-aligned windows that map directly to GPUs. In training, Phalanx delivers 10–40% higher end-to-end throughput at 4K–32K context lengths over optimized SWA-hybrids and Transformers by reducing costly inter-warp communication. Today, we are releasing both the technical report, a blog, and Phalanx kernels in spear, our research kernel library. We are hiring.